Visual Question Answering (VQA)

Visual Question Answering (VQA) is a task in computer vision that involves answering questions about an image. The goal of VQA is to teach machines to understand the content of an image and answer questions about it in natural language.

Image Source: visualqa.org

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 81–90 of 2167 papers

Title	Date	Tasks	Status	Hype
Med3DVLM: An Efficient Vision-Language Model for 3D Medical Image Analysis	Mar 25, 2025	Contrastive LearningImage-text Retrieval	CodeCode Available	2
MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis	Jul 4, 2024	DiagnosticLanguage Modeling	CodeCode Available	2
OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models	May 13, 2023	Key Information ExtractionNutrition	CodeCode Available	2
CausalVQA: A Physically Grounded Causal Reasoning Benchmark for Video Models	Jun 11, 2025	counterfactualDescriptive	CodeCode Available	2
LM4LV: A Frozen Large Language Model for Low-level Vision Tasks	May 24, 2024	Language ModelingLanguage Modelling	CodeCode Available	2
A Bounding Box is Worth One Token: Interleaving Layout and Text in a Large Language Model for Document Understanding	Jul 2, 2024	document understandingKey Information Extraction	CodeCode Available	2
LLaVAR: Enhanced Visual Instruction Tuning for Text-Rich Image Understanding	Jun 29, 2023	16kImage Captioning	CodeCode Available	2
LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning	Jun 13, 2022	Transfer LearningVisual Question Answering (VQA)	CodeCode Available	2
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering	Sep 20, 2022	Multimodal Deep LearningMultimodal Reasoning	CodeCode Available	2
Learning to Compose Dynamic Tree Structures for Visual Contexts	Dec 5, 2018	Graph GenerationPanoptic Scene Graph Generation	CodeCode Available	2

Show:10 25 50

← PrevPage 9 of 217Next →

All datasets GQA Test2019 VQA v2 test-dev VQA v2 test-std OK-VQA MSVD-QA DocVQA test MSRVTT-QA InfographicVQA GQA test-dev VizWiz 2020 VQA A-OKVQA CLEVR

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	human	Accuracy	89.3	—	Unverified
2	DREAM+Unicoder-VL (MSRA)	Accuracy	76.04	—	Unverified
3	TRRNet (Ensemble)	Accuracy	74.03	—	Unverified
4	MIL-nbgao	Accuracy	73.81	—	Unverified
5	Kakao Brain	Accuracy	73.33	—	Unverified
6	Coarse-to-Fine Reasoning, Single Model	Accuracy	72.14	—	Unverified
7	270	Accuracy	70.23	—	Unverified
8	NSM ensemble (updated)	Accuracy	67.55	—	Unverified
9	VinVL-DPT	Accuracy	64.92	—	Unverified
10	VinVL+L	Accuracy	64.85	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	PaLI	Accuracy	84.3	—	Unverified
2	BEiT-3	Accuracy	84.19	—	Unverified
3	VLMo	Accuracy	82.78	—	Unverified
4	ONE-PEACE	Accuracy	82.6	—	Unverified
5	mPLUG (Huge)	Accuracy	82.43	—	Unverified
6	CuMo-7B	Accuracy	82.2	—	Unverified
7	X2-VLM (large)	Accuracy	81.9	—	Unverified
8	MMU	Accuracy	81.26	—	Unverified
9	Lyrics	Accuracy	81.2	—	Unverified
10	InternVL-C	Accuracy	81.2	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BEiT-3	overall	84.03	—	Unverified
2	mPLUG-Huge	overall	83.62	—	Unverified
3	ONE-PEACE	overall	82.52	—	Unverified
4	X2-VLM (large)	overall	81.8	—	Unverified
5	VLMo	overall	81.3	—	Unverified
6	SimVLM	overall	80.34	—	Unverified
7	X2-VLM (base)	overall	80.2	—	Unverified
8	VAST	overall	80.19	—	Unverified
9	VALOR	overall	78.62	—	Unverified
10	Prompt Tuning	overall	78.53	—	Unverified